BASIS: Evaluating Belief-Aware Solutions in Interactive Settings beyond Instructions

Minyuan Ruan; Ziyue Wang; Kaiming Liu; Yunghwei Lai; Peng Li; Yang Liu

cs.CLFeb 14, 2026

BASIS: Evaluating Belief-Aware Solutions in Interactive Settings beyond Instructions

Authors: Minyuan Ruan, Ziyue Wang, Kaiming Liu, Yunghwei Lai, Peng Li, Yang Liu

Organizations: Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

Abstract

Large language model (LLM) agents are increasingly capable of carrying out complex, long-horizon tasks, yet effective assistance requires more than faithfully executing user instructions. Users' requests reflect their current understanding of the problem, which may be incomplete, outdated, or incorrect. When this understanding diverges from the actual task state, literal instruction following can fail to accomplish the user's underlying goal. We introduce BASIS (Belief-Aware Solutions in Interactive Settings), a benchmark that evaluates whether agents can infer the mental states underlying user requests, recognize belief-reality mismatches, and use this understanding to develop appropriate solutions. Rather than assessing mental-state inference solely through explicit questions, BASIS evaluates both the utility of such inferences and the quality of downstream solutions in user-agent interactions. Evaluation results show that current models often fail to account for users' mental states when generating solutions, and that solution quality is consistently higher when such information is explicitly elicited and made available during problem solving. We further investigate the practical value of mental-state consideration through comparisons with generic Chain-of-Thought prompting, controlled interventions on mental-state information, targeted training, and multi-agent collaboration experiments under partial observability. Together, these studies highlight the importance of not merely inferring others' mental states, but also using those understandings to provide grounded, goal-directed assistance.

Figures & tables

Figure 1: (a) An illustrative case shows a user interpreting a ModuleNotFoundError as a missing package and requesting reinstallation. Literal execution repeats the installation and leaves the error unresolved, whereas mental-state consideration helps the agent to check the active Python environment and use the one where the package is already installed. (b) An exploratory annotation study of SWE-Chat identifies instruction gaps, where mismatches exist between users’ explicit requests and what is needed to achieve their underlying goals, in 47% of interactions; among these gap cases, 89.4% show direct or strong contextual evidence related to users’ mental states.

Figure 2: The BASIS data construction pipeline. Stage 1: Scenario synthesis converts source task data into user-agent scenarios with belief-reality mismatches, specifying the actual task state

s

, user profile

\mathcal{P}

, observations

o

, belief

b

, and request

g

. Stage 2: Trajectory construction generates multi-turn interaction histories

\tau

that simulate user behavior and observation. An LLM-based validation module assesses data quality at both stages across five dimensions, with low-scoring instances refined and revalidated. The culture example illustrates the progression from source data to a mismatch scenario and its associated interaction trajectory.

Type	Scenarios	Cnt.	Belief- Profile Align.	Belief- Truth Corr.	Traj.- Belief Consist.	Traj.- Profile Consist.	Traj. Realism	AVG
LLM	Preference	120	4.90	4.80	4.06	4.34	4.04	4.43
	Education	100	4.90	4.75	4.21	4.31	4.01	4.44
	Culture	70	4.99	4.94	4.04	4.01	4.01	4.40
	SWE	100	4.85	4.81	4.01	4.24	4.01	4.38
Human	Preference	120	4.75	4.74	4.01	4.18	3.92	4.32
	Education	100	4.89	4.70	4.21	4.17	3.94	4.38

Table 1: Data distribution and quality analysis. We use an LLM scorer to assign scores from 0 to 5 according to the criteria defined in Appendix B.2 , and additionally report human ratings along the same dimensions. Cnt.: count; Align.: alignment; Corr.: correlation; Consist.: consistency; Traj.: trajectory.

Dimension	Accuracy	Weighted Kappa	Spearman Corr.
Belief	86.67%	0.86	0.85
Profile	85.67%	0.83	0.84
Solution	83.33%	0.88	0.89

Table 2: Human-LLM evaluator agreement on sampled instances across three evaluation dimensions.

Models	Evaluation Dimension	Preference				Culture				SWE				Education
Models	Evaluation Dimension	Separate	Vanilla	5-Step	10-Step	Separate	Vanilla	5-Step	10-Step	Separate	Vanilla	5-Step	10-Step	Separate	Vanilla	5-Step	10-Step
GPT-5.6-Sol	Latent Belief	38.61	42.08	63.54	67.29	51.90	68.10	68.81	70.24	57.34	65.51	74.75	75.25	34.58	42.67	56.42	63.42
	Profile	20.69	21.81	36.18	44.37	23.33	27.86	37.26	35.83	33.83	34.16	42.99	50.58	17.17	11.92	32.75	36.25
	Solution	19.43	29.40	71.00	78.44	25.11	41.25	55.38	65.51	24.00	39.31	58.33	68.04	29.11	39.84	57.03	61.78
	AVG	26.24	31.10	56.91	63.37	33.45	45.74	53.82	57.19	38.39	46.33	58.69	64.62	26.95	31.48	48.73	53.82
Claude-Sonnet-5	Latent Belief	39.44	45.83	61.94	63.68	48.31	61.55	59.06	72.62	49.92	54.29	72.63	72.36	46.09	45.25	54.29	64.37

Table 3: Evaluation results of 6 models across four domains (Preference, Culture, SWE, Education) and four evaluation configurations (Separate, Vanilla, 5-Step, 10-Step), regarding three evaluation dimensions. Cell shading indicates relative performance: darker purple signifies higher scores while lighter shades represent lower scores. “AVG”: the average over dimensions. “DeepSeek-R1-32B”: DeepSeek-R1-Distill-Qwen-32B

Model	Method	Pref.	Cult.	SWE	Edu.	Avg.
GPT-5.6-Sol	CoT	20.89	25.18	28.37	29.43	25.97
GPT-5.6-Sol	Mental	29.40	41.25	39.31	39.84	37.45
Gemini-3.8	CoT	25.88	54.52	53.07	43.01	44.12
Gemini-3.8	Mental	28.65	61.38	52.91	47.25	47.55
Qwen3.8-27B	CoT	15.10	19.65	23.09	24.97	20.70
Qwen3.8-27B	Mental	22.83	37.13	27.57	37.32	31.21

Table 4: Solution scores under CoT and mental-state consideration across three models. Mental refers to mental-state consideration. Pref.: Preference; Cult.: Culture; Edu.: Education. Avg. denotes the mean across domains. Bold indicates the higher score between the two strategies for each model within each domain and on average.

Figure 3: Performance changes when generating solutions conditioned on the mental-state information indicated in the legend. Providing task-aligned belief or profile information generally improves solution quality, whereas mismatched information leads to performance degradation.

\rectanglecolor blue!76-18-14 Models			Metrics	Preference			Culture			SWE			Education
		vanilla	5-Turn	10-Turn	vanilla	5-Turn	10-Turn	vanilla	5-Turn	10-Turn	vanilla	5-Turn	10-Turn
Qwen3-8B	Solution	11.91	37.54	47.45	7.46	18.48	24.82	8.37	18.25	25.67	16.62	24.29	28.48
	Belief	23.94	31.28	31.92	31.85	34.55	40.88	37.69	40.12	41.28	17.95	17.97	23.80
	Profile	12.56	17.27	17.97	10.55	15.23	19.41	15.87	18.39	22.09	6.19	10.29	15.13
Ours	Solution	15.65 +3.74	47.14 +9.60	58.25 +10.80	10.40 +2.94	26.71 +8.24	33.84 +9.02	11.29 +2.93	25.63 +7.38	33.74 +8.08	23.10 +6.48	34.61 +10.32	38.73 +10.25
	Belief	25.25	37.47	41.89	36.97	41.99	52.43	41.72	44.84	49.44	22.20	31.88	37.75

Table 5: RL training results of Qwen3-8B model. Bolded values indicate performance improvements within the Solution . Values in the Ours-Solution row additionally show the difference from the corresponding baseline Solution score.

Method	HiddenBench	AgentWebBench
Qwen3.8-27B
Baseline	67.44	29.64
Mental-state	75.26	34.93
DeepSeek-V4-Flash
Baseline	71.48	41.57
Mental-state	79.17	45.89

Table 6: Multi-agent benchmark scores. Mental-state denotes explicit prompting to consider other agents’ mental states. Shaded rows mark this variant; bold indicates the higher score within each model and benchmark.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Dimension	Label	Definition
Instruction quality	Complete and correct	The stated instruction contains the requirements necessary for successful task completion and does not contain a materially incorrect requirement.
	Incomplete	The instruction omits one or more requirements that are necessary for completing the task correctly.
	Incorrect	The instruction contains a materially incorrect requirement, assumption, or requested implementation.
	Both	The instruction is both incomplete and contains at least one materially incorrect requirement.
Mental state	Incomplete observation	The user’s limited view of the project or execution environment prevents a complete understanding of the underlying problem, leading to an instruction that omits necessary requirements or rests on assumptions inconsistent with the actual task state.
	Personal preference / other	The user’s preferences, prior experience, or domain expertise shape their interpretation of the available evidence, leading to an instruction based on assumptions that are inconsistent with the actual task state.

Appendix

Table 7: Annotation dimensions and labels used in our SWE-Chat study.

Figure 4: Annotation interface for the SWE-Chat study. Annotators inspect the interaction trajectory and available project information before assigning labels for instruction quality, the source of the requirement gap, literal executability, and agent gap handling.

Dimension	Label	Count	Sum	Percentage
Instruction quality	Complete and correct	53	100	53.0%
	Incomplete	24	100	24.0%
	Incorrect	14	100	14.0%
	Both	9	100	9.0%
Mental state	Incomplete observation	25	47	53.2%
	Personal preference / other	17	47	36.2%

Appendix

Table 8: Results of the SWE-Chat annotation study. Instruction quality is annotated for all 100 trajectories. The remaining dimensions are evaluated on the 47 trajectories containing an incomplete or incorrect instruction.

Judge Prompt

Task: Generate Atomic Fact Verification Rubrics.

Role: Strict Logic Auditor. The model decomposes a complex ground truth into a set of atomic facts that can be verified via simple search or strict logical checks. The objective is to maximize inter annotator agreement such that different evaluators using the same checklist will obtain identical scores.

Input Data includes observation, explicit instruction, true latent state, user latent belief, root cause of misconception, and user profile.

High agreement guidelines are as follows. No adjectives are allowed. Criteria must be anchored to explicit keywords or named entities from the input. Detection and correction must be separated into different criteria. If a user misconception exists, the model must explicitly refute it.

The output is a JSON object containing strictly verifiable boolean criteria across three dimensions. The first dimension evaluates latent belief recognition. The second dimension evaluates user profile and contextual modeling. The third dimension evaluates correct identification of root cause and resolution.

Each criterion must be a statement that can be answered with a binary yes or no judgment.

Appendix

Table 9: Prompts for rubrics generation.

Model	Type	Reference
GPT-5.6-sol	Proprietary	[ 26 ]
Gemini 3.8 Flash	Proprietary	[ 13 ]
Claude Sonnet 5	Proprietary	[ 3 ]
Qwen3.8-27B	Open-source	[ 31 ]
Qwen3.5-9B	Open-source	[ 30 ]
Qwen3-8B	Open-source	[ 45 ]

Appendix

Table 10: Full list of models evaluated in our experiments.

Figure 5: MiniMax-M2.1 trends with varied input trajectory turns.

Task	Dimension	0	1	2	3	4	5	6	7	8	9	10
Culture	Latent belief	49.17	54.40	59.64	60.12	57.98	57.86	63.81	59.18	69.40	62.50	61.31
	User profile	29.29	32.26	36.43	36.07	40.95	37.26	46.07	40.94	42.50	44.17	42.98
	Resolution	36.24	51.67	54.74	51.47	54.64	57.93	65.00	63.94	60.33	63.53	66.26
	Average	38.23	46.11	50.27	49.22	51.19	51.02	58.29	54.69	57.41	56.73	56.85
SWE	Latent belief	56.93	56.42	63.98	59.90	62.07	61.84	61.98	63.11	59.55	60.07	60.44
	User profile	41.23	41.41	39.58	45.49	44.62	46.67	41.49	42.27	45.92	46.01	46.32

Appendix

Table 11: Task performance of MiniMax-M2.1 across trajectories.

Scenarios	#Traj.	None	Belief & Profile	Belief	Profile
Preference	Vanilla	11.91	17.39 (+5.48)	16.79 (+4.88)	16.19 (+4.28)
Preference	10-Turn	47.45	53.62 (+6.17)	54.32 (+6.87)	56.37 (+8.92)
Culture	Vanilla	7.46	19.40 (+11.94)	19.90 (+12.44)	20.05 (+12.59)
Culture	10-Turn	24.82	36.11 (+11.29)	33.56 (+8.74)	31.66 (+6.84)
SWE	Vanilla	8.37	11.58 (+3.21)	7.27 (-1.10)	8.23 (-0.14)
SWE	10-Turn	25.67	31.49 (+5.82)	29.65 (+3.98)	28.78 (+3.11)

Appendix

Table 12: Enhancing the Solution performance of Qwen3-8B via multi-model collaboration, by incorporating the other two models, InternLM3-8B and DeepSeek-R1. “#Traj” refers to the number of trajectory turns provided to the input.

Figure 6: Average performance and variance of Base Qwen3-8B and our trained model.

Scenario Generation Prompt

Role: Synthetic Data Generator for Latent Belief Mismatch

You are generating a dataset to test if AI models can deduce a user’s hidden misconception based on their actions.

Mathematical Model:

- True State (

s^{*}

): The actual root cause of the issue (The reality).

- Observation (

o

): The initial symptom perceived by the user. Must be ambiguous (supports multiple theories).

Appendix

Table 13: Prompts for scenario and trajectory generation.

Data Validation Prompt

Role: Expert Data Evaluator

You are an expert in analyzing user behavior simulation datasets. Your task is to evaluate the quality of a generated scenario and user trajectory based on specific logical consistency and realism criteria.

Input Data:

- User Profile: {user_profile}

- True State: {true_state}

Appendix

Table 14: System prompt for data validation.

LLM Agent Evaluation

Large language model agent evaluation measures whether tool-using systems complete tasks reliably, safely, and efficiently.

See all 1,194 papers

UserHarness: Harnessing User Minds for Stronger Agent Theory-of-Mind
May 26, 2026Cheng Qian, Jiayu Liu, Heng JiTheory of Mind LLM Agent Evaluation
University of Illinois Urbana-Champaign
xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems
Sep 7, 2026Yongchang Peng, Qingshui Gu, Liya Zhu +31Pragmatic Reasoning in Language Models LLM Agent Evaluation
ByteDance Seed · M-A-P
From Belief Inference to Task Action: A Multi-Level Evaluation of Theory of Mind on Verifiable Tasks
Oct 26, 2026Zhenshuai Yin, Yongxin Guo, Mingda Zhang +4Theory of Mind Language Model Reasoning Evaluation
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Longgang, Shenzhen, Guangdong 518172, P.R. China · ByteDance Inc. · Shenzhen Research Institute of Big Data, Shenzhen, China

May 26, 2026J/K move · Enter open · S save

UserHarness: Harnessing User Minds for Stronger Agent Theory-of-Mind

Cheng Qian, Jiayu Liu, Heng Ji

University of Illinois Urbana-Champaign

Theory of Mind LLM Agent Evaluation User Modeling

Open paper AskPDF arXiv

Understanding what a user believes and intends is central to building effective agent assistants. This ability is often evaluated through Theory-of-Mind (ToM) tasks, where success requires reasoning from the user's perspective. However, many existing approaches address ToM with complex pipelines that model behavior indirectly, without explicitly reconstructing the user's mental state. This misses the core structure of the problem: users act based on their beliefs, which are updated through observations of the environment; beliefs and intentions jointly determine actions, which in turn change the environment; and social reasoning often requires nested beliefs about what others believe or intend. We propose UserHarness, a simple framework that reframes ToM reasoning as explicit user-mind reconstruction. UserHarness decomposes the user's mental state, its relation to the external environment, and the actions that follow from it, enabling agents to track what the user observes, believes, intends, and does. Across five benchmarks, UserHarness reaches up to 95.94% macro accuracy, improving over existing inference methods by more than 15% relative and over the strongest prompt-only harness by about 20% relative. These results suggest that robust user understanding requires reasoning from the roots of the user's mind, positioning user harnessing as a promising foundation for more adaptive future assistants.

Abstract

LLM Agent Evaluation

Explore similar work

UserHarness: Harnessing User Minds for Stronger Agent Theory-of-Mind

xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems

From Belief Inference to Task Action: A Multi-Level Evaluation of Theory of Mind on Verifiable Tasks